Consulting and Training

What does a Big Data Consultant do?

A Big Data consultant needs composing and relational abilities to carry some advanced tools suchas SQL. At Infogenx, we know how to keep up with precise examinations of the datasets by using savvy calculations and information advancements for your business.As your professional advisors, weare going to survey and deal with the quality and rightness of the accessible information to help your business perform well & sound. So get in touch with us now!

Big Data Augmentation

Big Data Augmentation is a method of increasing the value of information by including extra data from the different interior or exterior sources. With this extra data, your business can get the top to bottom understanding of extra business needs. Every business entity needs extra information to fuel itself. Information is a key resource today to study the speculation for what’s yetto come. At Infogenx, Data Augmentation works on techniques like using numerical estimations of midpoints and means, heuristics and systematic measurements, etc. to get the future course of happenings for your business (that is more likely).

Data Architecture and Design

Data Architecture and Design is a wide term that alludes to certain rules, strategies, philosophies, and models that administer and characterize the sort of information gathered and how it is utilized, put away, oversaw, and coordinated inside a business unit. It gives a convenientway to deal with the evolution of information and how it is handled over.

Cloud Architecture

Cloud Architecturegives birth to cloud services that vow to provide a large number of the operational advantages for a business. An aid in optional working is a boon for business units.The service is accessible inside an open distributed computing condition and can bring about expanded operational productivity inside an association. Front-end stage, backend stages, cloud-based, and a systemare main components and when consolidated, these parts make up Cloud Architecture.

Infrastructure Management

Infrastructure Management is utterly usefulfor any business from multiple points of view.It is essentially a remedy to employ, in case your information is lost, erased, debased, or undermined. IM can re-establish it and mind that using this methodology isa proactive approach to put everything back in the pipeline. IM framework is going to work wonders for you in the face of anycalamity.By redesigning thestrategy, it will aid inmaintaining strategic costs.

Data Ops / Dev Ops

Data Ops andDevOps are two sides of the same coin. These are particularly put to use for improvement, testing, quality confirmation, and organizing conditions for business. Employing DataOps andDev Ops in your business operationsis basic since it’s an iterativeprocedure that makes connections between the end-clients, the engineers who test &convey the invention, and the entrepreneurs who give the assessment.

Training

Big Data

Big Data refers to humongous volumes of data that cannot be processed effectively with the traditional applications that exist. The processing of Big Data begins with the raw data that isn’t aggregated and is most often impossible to store in the memory of a single computer.A buzzword that is used to describe immense volumes of data, both unstructured and structured, Big Data inundates a business on a day-to-day basis. Big Data is something that can be used to analyze insights that can lead to better decisions and strategic business moves.

Machine learning with python

Machine Learning is simply making a computer perform a task without explicitly programming it. In today’s world every system that does well has a machine learning algorithm at its heart. Take for example Google Search engine, Amazon Product recommendations, LinkedIn, Facebook etc, all these systems have machine learning algorithms embedded in their systems in one form or the other. They are efficiently utilising data collected from various channels which helps them get a bigger picture of what they are doing and what they should do.

Python is a widely used high-level programming language for general-purpose programming. Apart from being open source programming language, python is a great object-oriented, interpreted, and interactive programming language. Python combines remarkable power with very clear syntax. It has modules, classes, exceptions, very high level dynamic data types, and dynamic typing. There are interfaces to many system calls and libraries, as well as to various windowing systems. New built-in modules are easily written in C or C++ (or other languages, depending on the chosen implementation). Python is also usable as an extension language for applications written in other languages that need easy-to-use scripting or automation interfaces.

Artificial intelligence

Artificial intelligence (AI) makes it possible for machines to learn from experience, adjust to new inputs and perform human-like tasks. Most AI examples that you hear about today – from chess-playing computers to self-driving cars – rely heavily on deep learning and natural language processing. Using these technologies, computers can be trained to accomplish specific tasks by processing large amounts of data and recognizing patterns in the data.

Big Data with HDP

  • Introduction of Distribution systems
  • Comparisons with parallel systems with illustrations
  • Introduction to Data lakes
  • Introduction to relational and non-relational data sources
  • Working on architecture of hdfs
  • Hadoop and about its ecosystem like hdfs, hive, pig, mapreduce, oozie,Sqoop,flume
  • Working with open source systems like Hadoop
  • Illustrating use cases of Hadoop and hdfs
  • Working with performance tuning

HDFS

  • Architecture of hdfs, working with single and multi nodes with illustrations
  • Capacity management of nodes
  • Introduction to clustering, mirroring and monitoring the cluster performances
  • Fault Torrance

Map reduce

  • Introduction to map and reduce
  • Running processes under mapper and reducer
  • Monitoring map reducer jobs

Pig

  • Introduction to pig concept
  • ETL operations with Pig

Oozie

  • Concepts of oozie
  • Workflows related to oozie

Sqoop

  • Concepts of Sqoop
  • How to ingest data with Sqoop

Flume

  • Concepts of flume
  • Log analytics with flume

Spark

  • Concepts of spark, Resilient Distributive data set (RDD)
  • Data Frame, Data set
  • Actions and transformations
  • Programing with python and Scala
  • Performance bottlenecks and techniques utilized optimization